Stereotactic Body Radiotherapy for Extracranial Oligometastatic Pancreatobiliary Cancer
Bibliographic record
Abstract
AIMS: In an oligometastatic disease, stereotactic body radiotherapy (SBRT) is being increasingly used in many different histologies to delay progression. However, by virtue of pancreatobiliary cancers not being as common and more aggressive natural history, data on its usage in pancreatobiliary cancers have been limited. Herein, our study aims to evaluate the outcomes of extracranial SBRT for oligometastatic pancreatobiliary cancers. MATERIALS AND METHODS: A retrospective review was conducted on patients with oligometastatic pancreatobiliary cancer treated with extracranial SBRT between 2014 and 2023 at our institution. The analysis examined cumulative incidence of local failure (LF), progression-free survival (PFS), cumulative incidence of starting or changing systemic therapy (SCST), overall survival (OS) and Radiation Therapy Oncology Group-graded treatment-related toxicities. Kaplan-Meier methodology and Cox proportional hazard models were used for survival analysis. RESULTS: Among the 62 patients (115 lesions) identified, the median follow-up was 13.4 months. By primary location, 71% had pancreatic cancer, 15% had cholangiocarcinoma, and 15% had ampullary cancer. Synchronous and metachronous oligometastatic disease was 37% and 63%, respectively. Eighty-seven percent of patients had 1-2 lesions treated with SBRT. At 12 months, LF, PFS, SCST, and OS was 21.5%, 28.0%, 33.6% and 61.7%, respectively. On multivariable analysis, the only predictor of worse PFS was higher PTV volume. No grade 3-5 toxicities were reported. CONCLUSION: Similar to other cancers, SBRT appears to be a promising option for those with oligometastatic pancreatobiliary cancer. More research in how to optimise integration of SBRT in a multidisciplinary setting for this challenging disease would be of benefit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".